I thought building an agent was set it and forget it, but it’s more than that. Here’s the truth about agents that nobody told you about. The first time I built my AI agent, I gave the LLM instructions and let it build it. I thought once I explained what I wanted, it would do the work. I never thought I’d spend so much time managing and improving it afterward. I get why agents are becoming popular. Having something handle a whole process for you sounds appealing, and frameworks make the first version easier to build. But once you start using it, you find out how much work is still needed. Agent failures and recovery: I was building an agentic workflow with OpenClaw and found myself babysitting it every time it stopped. I had to wake it up, check its status, and figure out why it wasn’t continuing. Doing the work myself would have been faster. Then you start thinking about durability. Can it continue from where it failed? Does it know what already finished? Can it retry without repeating a completed action? Will it try another solution or keep failing until you step in? Domain knowledge: You must know what you’re automating. If you don’t understand the process, how will you explain the exceptions or evaluate the result? Something can look correct without actually being correct. You’ll keep finding things you forgot to explain. The agent needs examples, corrections, and knowledge you’ll have to keep feeding it. Ongoing maintenance: I find the learning process in Hermes very impressive, but I wouldn’t rely on it alone. Is it learning something useful? Are its instructions still relevant? Is there a better tool it should use? Saving something to memory doesn’t mean it learned the right lesson. A workaround for one failure could cause problems next time. Giving the agent too much responsibility: It’s easy to say “be my employee” and expect it to figure out the rest. But what’s the actual job? What does a finished result look like? What can it decide, and when does it need you? Start with something you can explain and evaluate. More tools and agents also mean more things to debug. Some steps can use ordinary automation. Thinking one successful run means it works: What happens when information is missing, a tool fails, or the task changes slightly? Keep the cases where it failed and test them again after making changes. Fixing one problem can introduce another. For me, the main pillar is evaluation. Did it finish correctly? How often did I have to help? After checking and fixing its work, did it actually save me time? You can end up with more failed agents than useful ones. I want to judge mine by whether I can rely on them, including when something goes wrong.